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Version control is a component of software configuration management. [1] A version control system is a software tool that automates version control. Alternatively, version control is embedded as a feature of some systems such as word processors, spreadsheets, collaborative web docs, [2] and content management systems, e.g., Wikipedia's page ...
In the world of open source software, the Concurrent Version System (CVS) has long been the tool of choice for version control. And rightly so. CVS itself is free software, and its non-restrictive modus operandi and support for networked operation—which allow dozens of geographically dispersed programmers to share their work—fits the ...
AccuRev is a centralized version control system which uses a client–server model. Communication is performed via TCP/IP using a proprietary protocol. Servers function as team servers, continuous integration servers, or build servers. AccuRev is built around a stream-based architecture in which streams form a hierarchical structure of code ...
The following table contains relatively general attributes of version-control software systems, including: Repository model, the relationship between copies of the source code repository. Client–server, users access a master repository via a client; typically, their local machines hold only a working copy of a project tree. Changes in one ...
The Librarian [proprietary, shared] – Around since 1969, source control for IBM mainframe computers; from Applied Data Research, later acquired by Computer Associates; Vault [proprietary, client-server] – version control tool by SourceGear; first installation can be used for free
There may be an access control system, a checkout and version control facility, and the ability to define generic relations. Three protocol bindings are defined, one using WSDL and SOAP, another using AtomPub, [2] and a last browser-friendly one using JSON. The model is based on common architectures of document management systems.
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DVC is a free and open-source, platform-agnostic version system for data, machine learning models, and experiments. [1] It is designed to make ML models shareable, experiments reproducible, [2] and to track versions of models, data, and pipelines. [3] [4] [5] DVC works on top of Git repositories [6] and cloud storage. [7]